Detecção Automática e Dinâmica de Memória de Trabalho utilizando Q-Learning e Média Móvel Exponencialmente Ponderada

Resumo

Intelligent Tutoring Systems work to customize Virtual Learning Environments according to the learner's cognitive profile. To customize the environment, it needs to apply Artificial Intelligence techniques to detect personality traces, affects, work memory and learning styles.This work proposes a comparative study between two techniques of working memory detection: Q-Learning and Exponentially Weighted Moving Average. These algorithms use navigation traits to classify the learner's working memory. The results demonstrate that these techniques can be applied in STIs to classify working memory.